AI implementation framework, explained
NIST groups the risk side of AI work as Govern, Map, Measure, and Manage. That guide is voluntary. It is about trust, not a project plan for a shop. We keep it beside a simpler order.
Our order has plain stages, because that is how the talk goes with an owner. A demo can feel like progress. A list can feel like a delay. The list is the work. If a stage has no answer, stop there. A tool open on a laptop is not a stage.
What are the stages?
First, name the job. Then score whether the data can carry it, and write the steps down. Next, name the person who can say the output is wrong. Last, keep the work or stop it. Skip a stage and you get the stall we describe for pilots.
How is this different from a readiness framework?
A readiness framework scores the business before you buy. An implementation framework starts after that read exists and the job has a name. One asks if you should start. The other asks what order to follow.
Questions
What is an AI implementation framework in simple terms?
It is the order you follow to put AI on a job that already has a name. You do not skip from a demo to a rollout.
Who is the checker?
The checker is the named person who can say the output is wrong, with time set aside to look. If you cannot name that person, you are not ready to keep the work.
Does NIST replace this order?
No. NIST names Govern, Map, Measure, and Manage for the risk work. Our stages are the plain order for one job. Use both. Do not swap the list for a demo.
Where we use this term
Sources
- AI Risk Management Framework. NIST. .
Definition checked against these sources and our own pages on .